The Way We Forecast Monsoon Is Going To Change. Know How

Predicting Monsoon: What are the odds that a Professor studying how sound creates instabilities in combustion would be interested in developing a method of understanding the Indian summer monsoon as a planetary-scale weather phenomenon? Well, R.I. Sujith, a Professor of Aerospace Engineering at IIT Madras was.

His approach employed a complex dynamic network framework to analyse climate systems, particularly the monsoon, treating it as a planetary-scale phenomenon. This idea, however, did not sit well with many leading meteorologists in the country. But soon he met Bhupendra Nath Goswami who believed that this could give India a novel way to define the monsoon season more objectively and accurately.

Sujith-Goswami Collaboration And Monsoon Research 

Sujith-Goswami collaboration aimed at defining the monsoon season more objectively and accurately and that wouldn't have been possible without research. So the first paper published by the duo addressed ambiguities in the onset of the Indian summer monsoon, which marked the start of their amusing journey.

Then came the analysis of how IMD does the weather forecast. IMD declares the onset of monsoon based on rainfall at 14 stations in Kerala and southern Karnataka, along with wind, cloud persistence, and depth. In this, objective criteria are used only for Kerala; local precipitation-based thresholds are used elsewhere, often leading to “bogus onsets.” According to Sujith, getting local onsets accurate is very crucial, and thus, the research delved deeper.

What Was Sujith's Approach?

Sujith's approach is rooted in studying the Indian summer monsoon through complex network theory, a method traditionally used in physics and engineering to understand how interactions within large systems drive collective behavior.

Research on the matter was also presented at the European Geosciences Union in April 2025, and the American Meteorological Society in January 2025.

But First, What Is Complex Network Theory?

In simple terms, it involves analysing nodes (individual entities like weather stations or regions) and links (how their behaviors are correlated or influence one another). In large monsoon systems, it can detect phase transitions, sudden shifts from one state to another.

Sujith used this to distinguish local onsets of rainfall that are caused by local weather and the large-scale onsets of monsoon that are triggered by wider climatic systems. 

How Was The Theory Tested? 

The first step was mapping local onsets as clusters, in which he and his team identified clusters where local rainfall onset occurred. Initially, these clusters represented unconnected, isolated weather systems but soon as the monsoon progressed, clusters began to grow and merge forming a connected network.

Such a sudden increase in cluster size usually signals a phase transition which is a clear marker of the onset of monsoon on a larger scale.

Further, the team retrospectively tested the model from 1940 onwards and observed it was able to track the advance and retreat of the monsoon each year.

The approach proved to be a huge success as it could objectively capture the early onset of monsoon in the Northeast, monsoon avoidance of rain-shadow areas like Tamil Nadu, and return of monsoon via Northeast monsoon.

This is a fresh technique that could help define and study the onset of monsoon, a crucial need for farmers and policy planners.

Sujith's approach is a physics-inspired, data-driven framework that treats the monsoon as not just a meteorological event, but also as a complex, evolving network of interconnected local weather systems.

This is a breakthrough that can help differentiate true monsoon onset from isolated rainfall, offering greater accuracy and predictive potential.

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